The command make check is optional. What is does is to compile SystemC source files to see if the files can run. I strongly suggest that you run it.

Step 4: Tell your compiler where to find SystemC

Since we do not install SystemC with a standard location we need to specifically tell the compiler where to look for the libraries. We do this with an environment variable.

$ export SYSTEMC=/usr/local/systemc/

This, however will disappear on the next login. To permanently add it to your environment, alter ~/.profile or ~/.bash_profile if it exists. For system wide changes, edit /etc/environment. (newline with expression: SYSTEMC_HOME=”/usr/local/systemc/“) To compile a systemC program simply use this expression:

Monday, August 22, 2011

Begin copied blog post.
It gives a working example on choosing of various modules at the recognition pipeline for human figure (pedestrians).

Much simplified summary

It uses Histogram of Gradient Orientations as a descriptor in a 'dense' setting. Meaning that it does not detect key-Points like SIFT detectors (sparse). Each feature vector is computed from a window (64x128) placed across an input image. Each vector element is a histogram of gradient orientations (9 bins from 0-180 degrees, +/- directions count as the same). The histogram is collected within a cell of pixels (8x8). The contrasts are locally normalized by a block of size 2x2 cells (16x16 pixels). Normalization is an important enhancement. The block moves in 8-pixel steps - half the block size. Meaning that each cell contributes to 4 different normalization blocks. A linear SVM is trained to classify whether a window is human-figure or not. The output from a trained linear SVM is a set of coefficient for each element in a feature vector.

I presume Linear SVM means the Kernel Method is linear, and no projections to higher dimension. The paper by Hsu, et al suggests that linear method is enough when the feature dimension is already high.

OpenCV implementation (hog.cpp, objdetect.hpp)

The HOGDescriptor class is not found in the API documentation. Here is notable points judging by the source code and sample program(people_detect.cpp):

Comes with a default human-detector. It says at the file comment that it is "compatible with the INRIA Object Detection and Localization toolkit. I presume this is a trained linear SVM classifier represented as a vector of coefficients;

No need to call SVM code. The HOGDescriptor.detect() function simply uses the coefficients on the input feature-vector to compute the weight-sum. If the sum is greated than the user specified 'hitThreshold' (default to 0), then it is a human-figure.

'hitThreshold' argument could be negative.

'winStride' argument (default 8x8)- controls how the window is slide across the input window.

'scale0' controls how much down-sampling is performed on the input image before calling 'detect()'. It is repeated for 'nlevels' number of times. Default is 64. All levels could be done in parallel.

Sample (people_detect.cpp)

Uses the built-in trained coefficients.

Actually needs to eliminate for duplicate rectangles from the results of detectMultiScale(). Is it because it's calling to match at multiple-scales?

detect() return list of detected points. The size is the detector window size.

Observations

With GrabCut BSDS300 test images - only able to detect one human figure (89072.jpg). The rest could be either too small or big or obscured. Interestingly, it detected a few long-narrow upright trees as human figure. It takes about 2 seconds to process each picture.

With GrabCut Data_GT test images - able to detect human figure from 3 images: tennis.jpg, bool.jpg (left), person5.jpg (right), _not_ person7.jpg though. An interesting false-positive is from grave.jpg. The cut-off tomb-stone on the right edge is detected. Most pictures took about 4.5 seconds to process.

MIT Pedestrian Database (64x128 pedestrian shots):

The default HOG detector window (feature-vector) is the same size as the test images.

Recognized 72 out of 925 images with detectMultiScale() using default parameters. Takes about 15 ms for each image.

Recognized 595 out of 925 images with detect() using default parameters. Takes about 3 ms for each image.